The present invention relates in general to communication and navigation systems and methods. In particular, the invention relates to a method and a system for detecting and eliminating radio frequency interferences in real time, and more explicitly related to satellite navigation systems.
Today the use of the electromagnetic spectrum is becoming increasingly more intense due to the enormous amount of wireless devices. This fact has driven the generalization of the problem of the radio frequency interferences (RFI). RFI are signals which degrade or prevent the correct functioning of any instrument which uses wireless communications.
Due to the generalization of the navigation applications, especially in the environment of global navigation satellite systems (GNSS), RFI are becoming a serious problem due to the critical consequences, mainly, in terms of security and reliability. However, the impact not only relates to navigation, but also to the availability of the GNSS signals used for synchronizing critical infrastructures, such as for example telecommunications, energy transport and bank transaction networks and also, more recently, in earth observation systems which use the signals of GNSS as signals of opportunity.
In a wide sense, when GNSS is referred to, they include all the global or regional systems. The most used and the ones that stand out are the United States global position system (GPS), the Russian GLONASS, the European Galileo, or the Chinese Beidou. In any case, any other satellite navigation system of those which are in development (QZSS, IRNSS, . . . ) or which could be launched in the future would be within the scope of the present invention.
In recent years, a series of signal processing methods for detecting and eliminating RFI signals has been developed, originally in the field of radio astronomy and more recently in earth navigation and observation applications, mainly microwave radiometry and GNSS-R reflectometry.
In spite of the GNSS signals having inherent protection from the RFI signals owing to the widened spectrum modulations used, generally, said techniques are not sufficiently robust so as to suitably treat the interferences. Furthermore, the extremely low strength of the GNSS signals which arrive to the Earth's surface, far below the noise level, makes them very sensitive and vulnerable to the presence of any interference, whether from intentional or intentional origin.
The most common origins of unintentional interferences could be classified into lower harmonic frequency bands, intermodulation products (IMP), broadband signal overlapping or out-of-band emissions. All of these can easily affect the signals of GNSS, causing the desensitizing of the receiver. Another example of unintentional RFI are the aerial radio navigation signals which, either share the same frequency band as some of the GNSS signals, or operate in bands adjacent to these. Some examples of said signals are DME (distance measuring equipment), TACAN (tactical air navigation system), ADS (automatic dependent surveillance), Link 16, radar systems (land or orbital), amateur radio emissions, mobile communications, satellite communications or MSS (mobile satellite services). In addition, other electronic devices such as computer components, digital buses, etc., can also cause interference in an unintentional manner.
With respect to the intentional interferences, they normally correspond to elements or devices specifically designed for interfering or disrupting a determined frequency band. Said devices are also known as jammers. These can operate at one or different bands protected a priori and are usually cheap and easily accessible which can pose a serious security problem in navigation and especially in military applications. These disrupting devices would include the devices known as personal privacy devices (PPD) which have proliferated alarmingly in recent years and therefore have aggravated the problem of RFI. These devices deliberately transmit signals in the frequencies which use GNSS or close to them in order to thereby make the GNSS unusable within an area around the PPD.
The effect of the RFI on the radio frequency receivers is translated into bad functioning of the same. When there are disruptions in the same GNSS band, the equivalent noise level increases and the receiver is desensitized. In addition, when the interference is in a band adjacent to those used in GNSS, the reception chain may end up being saturated, disabling the selective rejection achieved by means of the use of filters tuned in the functioning band of the receiver.
GNSS have not only become a key technology in navigation and positioning, being used in all types of means of transportation (land, maritime, aerial, etc.), but they are also used in other critical applications and emergency services for the communication and synchronization thereof (for example police teams, firefighters or rescue teams). In addition, the high stability of the reference clocks used in the satellites of said systems allows the synchronization of nodes in any network, including for high-precision applications, including electrical transmission networks, radio diffusion and telecommunications networks, astronomical observations and even temporal marks in bank transactions.
In summary, there are three main reasons which support the development of techniques for combatting the threat of RFI. Firstly, the power level of the GNSS signals is intrinsically weak. Furthermore, the RFI signal power sources can be very different in nature and given the increase in the use of the radioelectric spectrum, the RFI signal presence is increasingly more common. Lastly, the increasingly larger number of critical applications based on GNSS means it is necessary to address the problem, not only in terms of the detection and localization thereof, but also the suppression thereof to contribute to the fact that GNSS can be considered sufficiently robust so as to support all types of applications and services.
Among the methods of analysis and elimination of RFI mentioned, those based on the time domain, the frequency domain, on statistical analysis of the signal received or on the spatial adaptive filtering are the most relevant, and therefore, the most used. Some of these techniques have already been applied in systems in real time, but do not offer high rejection against RFI, either they are designed for a particular case of RFI, such as for example continuous frequency signals, or sinusoidal frequency signals (CW, continuous wave) or also frequency modulated (FM or chirp) signals. There are also other methods based on adaptive antennas and antenna groupings, especially intended for military applications, but they are not the object of the present invention.
The treatment of the RFI is approached from the point of view stochastic processes, therefore the RFI detection algorithms are based on the Neyman-Pearson criterion hypothesis, where a value is defined for discriminating between signal samples contaminated with RFI and clean samples. Using this criterion, a compromise is achieved between the probability of detecting the RFI correctly (probability of detection) and the probability of eliminating the data erroneously detected as RFI (probability of false alarm).
Generally, in this detection method, the coexistence of RFI and desired signal are analyzed in difference domains or spaces, whether in the time domain, frequency domain or in both at the same time and then using this information to eliminate the interference of the useful signal received.
In the time domain, the samples of the signal received are compared directly with a threshold value. Samples lower than the threshold are considered free of RFI and samples with a larger amplitude are “marked” as contaminated by RFI. These algorithms are effective when there are short bursts of high power located in time, this is why this method is called the “pulse blanking” method. However, sinusoidal signal interferences (CW), if they are detected in this manner, lead to the complete blanking of the signal itself.
In the frequency domain, the amplitude of the signal in each frequency is compared, as in the previous case, with a threshold value. The signal received is then processed with a digital filter, the transmission zeros of which are established in the frequencies in which the RFI are located. This method is generally called notch filtering and is the opposite of pulse blanking since it can easily detect sinusoidal interferences, but does not take into account the temporal evolution of the same.
Lastly, the methods that combine analysis in the time domain and in the frequency domain have the advantage of being capable of detecting and distinguishing between the interferences both of continuous wave and pulsed wave because they have a certain temporal and frequency resolution.
However, the resolution in said domains is related and limited by the so-called Gabor limit, the principle of uncertainty in the context of the processing of the signal. Two examples of this type of analysis are short-time Fournier transform (STFT) and wavelet transform (WT).
STFT is a representation of the signal in the domains of time and frequency with a given resolution in each one of these. This means that the pulsed RFI signals can be detected based on a duration determined by the time resolution, but not shorter, and an infinite number of contaminated frequency bands can be distinguished, determined by the frequency resolution. There are various forms of implementing STFT, which are dependent on the application. Among these, filter banks or fast Fournier transform are distinguished.
In addition, the wavelet transform (WT) has the advantage of being able to provide analysis with multiple resolution, but in turn, the implementation thereof is usually more complex. As in the previous case, a threshold value discriminates between clean data and data contaminated with RFI, but in this case, it must be carried out for each sample in the time-scale space.
In addition, there are statistical methods. Both the interferences, RFI and the useful signal, are assumed independent stochastic processes and have statistical properties which can be used for distinguishing between them. In order to achieve this aim, the RFI and the desired signals should not follow identical statistical distributions. In this case, the Neyman-Pearson criterion can be applied in the sense that a sample is correctly detected when it has been determined that it belongs to the corresponding statistical distribution thereof. In contrast, it is considered a false alarm when it has been erroneously determined that said sample belongs to a statistical distribution. This selection can be carried out according to different parameters. For example, two of these are called: central moments and the normality test.
The central moments of the signal received can help to detect RFI, in particular, the second and the fourth order can be very useful. If the signal has a statistical average equal to zero, the second central moment or variance is directly the average of the power of the signal and the square root thereof, the standard deviation, is related to the amplitude or scale of the signal.
The main problem with this approach is the calculation of the standard deviation of, for example, the desired clean signal with respect to the signal received contaminated with RFI. In order to resolve this, the value of the standard deviation can be obtained from robust estimates such as the median absolute deviation or the interquartile range which provide results less affected by the atypical values or outliers. The fourth central moment is related to the normality tests.
Normality tests are focused on finding out if a set of samples belongs or is similar to the normal statistical distribution. If only one of the distributions is normal, these tests can help to distinguish samples free of RFI and contaminated samples.
Kurtosis is a statistical parameter based on the relation between the fourth moment with respect to the square of the variance. This statistical parameter has the property of having a value equal to 3 for the signals with normal distribution and generally it is different from 3 for non-Gaussian signals. Other normality tests are Shapiro-Wilk, Anderson-Darling, Chi-square and Jarque-Bera for example.
In spite of the majority of the mathematical algorithms and models being well known in signal processing theory, the real challenge is to find the way of combining them with each other with the aim of implementing a system which maximizes the detection and elimination of RFI without having an a priori knowledge of the interfering signal.
The patent application WO-2012105747-A1 presents a system and a method which has a signal receiver for converting a positioning signal from a satellite received by an antenna into a digital intermediate frequency (IF) signal. A signal generator in series produces signals in series by commutation of the digitalized intermediate frequency signal. An interference canceller which distributes the signals in series after eliminating the interference of the signals in series. A satellite navigation receiver which receives the series distributed signals. A signal generator in parallel which produces the output signal in parallel which passes to an output terminal. This system and method only work with signals in the time domain or in the frequency domain and is only focused on RFI signals in modulated frequency (FM).
The U.S. Pat. No. 7,660,374-B2 proposes digitalizing the signal from the satellite, carrying out a transformation, identifying the interferences based on a certain threshold to eliminate said interferences and reconstructing the signal once again. Using this method, the interferences, both of broad band and narrow band and therefore the performance of the GPS signal are minimized. The main use is orientated at overcoming narrow band interferences, but it also explains how a second channel via which an additional algorithm for suppressing broad band interferences is applied.